Set Up

Install/load necessary R packages

Set date range

min_year = 2017
max_year = 2021

Peatland Daily Water Table (Bogwell Data)

More information about peatland and upland water elevation data collected at the Marcell Experimental Forest can be found on the Environmental Data Initiative Repository Portal.

Manual Stripcharts

  • Read in the most up-to-date data from the following Box file path: External-MEF_DATA/Hydro/WaterTableElevation/Bogwells/L1

    • Less up-to-date, already published data can be found at the following Box file path: External-MEF_DATA/EDI/peatland_water_table_daily/edi.562.2/data_objects/

  • Clean the data into a tidy format

    • Subset data to 2017 - present
#set file path to read in data from Box 
filepath = "/Users/miaforsline/Library/CloudStorage/Box-Box/External-MEF_DATA/Hydro/WaterTableElevation/Bogwells/L1"

#read in the raw data 
bogwell <- read_csv(here(filepath, "L1_Peatland_well_daily_history_2021forMia.csv"))

#clean the data
##note: we do not want any E values for the flag column 
bogwell_clean <- bogwell %>% 
  clean_names() %>% 
  mutate(year = format(as.POSIXct(date, format = '%Y-%m-%d', 
                                  tz = "GMT"), 
                       format = '%Y'),
         year = as.numeric(year)) %>% 
  subset(year >= min_year & year <= max_year)

#save clean data CSV to use in future RMD files
write.csv(x = bogwell_clean, 
          file = file.path(here("intermediate_data", "bogwell_clean.csv")),
          row.names = FALSE)

Plot all sites together

#plot
p_bogwell <- ggplot(data = bogwell_clean) + 
  geom_line(aes(x = date, y = wte, color = peatland)) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("Daily Peatland Water Table Elevation (", min_year, "-", max_year, ")"),
       color = "Site"
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#static plot 
p_bogwell

S1 Manual Stripchart

bogwell_clean_s2 <- bogwell_clean %>% 
    subset(peatland == "S1")

#plot
p_bogwell_s1 <- ggplot(data = bogwell_clean_s2) + 
  geom_line(aes(x = date, 
                y = wte, 
                group = 1,
                text = paste0("Date: ", date, "\n",
                              "Water Table Elevation (m): ", wte))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("S1 Daily Peatland Water Table Elevation (", min_year, "-", max_year, ")") 
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot 
ggplotly(p_bogwell_s1,
         tooltip = "text")

S2 Manual Stripchart

bogwell_clean_s2 <- bogwell_clean %>% 
    subset(peatland == "S2")

#plot
p_bogwell_s2 <- ggplot(data = bogwell_clean_s2) + 
  geom_line(aes(x = date, 
                y = wte, 
                group = 1,
                text = paste0("Date: ", date, "\n",
                              "Water Table Elevation (m): ", wte))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("S2 Daily Peatland Water Table Elevation (", min_year, "-", max_year, ")") 
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot 
ggplotly(p_bogwell_s2,
         tooltip = "text")
bogwell_clean_s2 %>% 
  #remove rows that have NA wte values 
  filter(!is.na(wte)) %>% 
  summarise(mean_wte_m = round(mean(wte), digits = 3),
            max_wte_m = max(wte),
            min_wte_m = min(wte), 
            sd_wte_m = round(sd(wte), digits = 3)
            ) %>% 
  #t() %>% 
  kable(caption = "S2 Stripchart Bogwell Statistics", 
        col.names = c("Mean WTE (m)", 
                      "Max WTE (m)",
                      "Min WTE (m)",
                      "SD")) %>% 
  kable_material(c("striped", "hover"))
S2 Stripchart Bogwell Statistics
Mean WTE (m) Max WTE (m) Min WTE (m) SD
421.88 422.15 421.48 0.095

S3 Manual Stripchart

bogwell_clean_s3 <- bogwell_clean %>% 
    subset(peatland == "S3")

#plot
p_bogwell_s3 <- ggplot(data = bogwell_clean_s3) + 
  geom_line(aes(x = date, 
                y = wte, 
                group = 1,
                text = paste0("Date: ", date, "\n",
                              "Water Table Elevation (m): ", wte))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("S3 Daily Peatland Water Table Elevation (", min_year, "-", max_year, ")") 
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot 
ggplotly(p_bogwell_s3,
         tooltip = "text")

S4 Manual Stripchart

bogwell_clean_s4 <- bogwell_clean %>% 
    subset(peatland == "S4")

#plot
p_bogwell_s4 <- ggplot(data = bogwell_clean_s4) + 
  geom_line(aes(x = date, 
                y = wte, 
                group = 1,
                text = paste0("Date: ", date, "\n",
                              "Water Table Elevation (m): ", wte))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("S4 Daily Peatland Water Table Elevation (", min_year, "-", max_year, ")") 
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot 
ggplotly(p_bogwell_s4,
         tooltip = "text")

S5 Manual Stripchart

bogwell_clean_s5 <- bogwell_clean %>% 
    subset(peatland == "S5")

#plot
p_bogwell_s5 <- ggplot(data = bogwell_clean_s5) + 
  geom_line(aes(x = date, 
                y = wte, 
                group = 1,
                text = paste0("Date: ", date, "\n",
                              "Water Table Elevation (m): ", wte))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("S5 Daily Peatland Water Table Elevation (", min_year, "-", max_year, ")") 
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot 
ggplotly(p_bogwell_s5,
         tooltip = "text")

S6 Manual Stripchart

bogwell_clean_s6 <- bogwell_clean %>% 
    subset(peatland == "S6")

#plot
p_bogwell_s6 <- ggplot(data = bogwell_clean_s6) + 
  geom_line(aes(x = date, 
                y = wte, 
                group = 1,
                text = paste0("Date: ", date, "\n",
                              "Water Table Elevation (m): ", wte))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("S6 Daily Peatland Water Table Elevation (", min_year, "-", max_year, ")") 
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot 
ggplotly(p_bogwell_s6,
         tooltip = "text")

Shaft Encoders

Function for loading Campbell DataLogger .dat files:

readCDL = function(file){
  
  # read data file starting on 5th line
  dat <- read.csv(file, sep=",",header=FALSE,skip=4,na.strings="NAN",stringsAsFactors=F)
  
  # Read in just the header line (l2)
  # unlist the line, and remove quotes 
  h <- readLines(file, n=2)[2]
  n <- as.factor(unlist(strsplit(h, ",")) )
  n2 <- gsub('"', "", n)
  
  # assign column names to dataframe
  colnames(dat) = n2
  
  return(dat)
}

Constants to convert between feet and meters:

MetersPerFoot = 0.3048
FeetPerMeter = 3.28048

min_year = 2020
max_year = 2021

S1 Shaft Encoder

  • Read in the 2022 data from the following Box file path: External-MEF_DATA/DataDump/RealTimeData

  • Clean the 2022 data into a tidy format

    • Subset data to 2020 - present
#set file path to read in data from Box 
filepath = "/Users/miaforsline/Library/CloudStorage/Box-Box/External-MEF_DATA/DataDump/RealTimeData"

#read in the raw data 
##note: this data is only 2022 
s1_se <- readCDL(file = here(filepath, "S1-EM3_Table1.dat"))

#clean the data 
s1_se_clean <- s1_se %>% 
  clean_names() %>% 
  #select relevant columns
  select(timestamp,
         wt_elev) %>% 
  #rename columns 
  rename(datetime = timestamp,
         wt_elev_ft = wt_elev) %>% 
  #reformat columns into appropriate classes 
  mutate(datetime = as.POSIXct(datetime, format = '%Y-%m-%d %H:%M:%S', 
                                  tz = "GMT"),
    wt_elev_ft = as.numeric(wt_elev_ft),
    wt_elev_m = wt_elev_ft * MetersPerFoot)

2019 data

#set file path to read in data from Box 
filepath = "/Users/miaforsline/Library/CloudStorage/Box-Box/External-MEF_DATA/DataDump/annual_appended_logger_files"

year = "2019"

file = "S1-EM3_Table1.dat"

#read in 2019 data 
s1_2019 <- readCDL(file = here(filepath, year, file))

#clean 2019 data 
s1_2019_clean <- s1_2019 %>% 
  clean_names() %>% 
  #select relevant columns
  select(timestamp,
         wt_elev) %>% 
  #rename columns 
  rename(datetime = timestamp,
         wt_elev_ft = wt_elev) %>% 
  #reformat columns into appropriate classes 
  mutate(datetime = as.POSIXct(datetime, format = '%Y-%m-%d %H:%M:%S', 
                                  tz = "GMT"),
    wt_elev_ft = as.numeric(wt_elev_ft),
    wt_elev_m = wt_elev_ft * MetersPerFoot)

2020 data

#set file path to read in data from Box 
filepath = "/Users/miaforsline/Library/CloudStorage/Box-Box/External-MEF_DATA/DataDump/annual_appended_logger_files"

year = "2020"

file = "S1-EM3_Table1.dat"

#read in 2020 data
#note: this data is 2020 - 2022 
s1_2020 <- readCDL(file = here(filepath, year, file))

#clean 2020 data 
s1_2020_clean <- s1_2020 %>% 
  clean_names() %>% 
  #select relevant columns
  select(timestamp,
         wt_elev) %>% 
  #rename columns 
  rename(datetime = timestamp,
         wt_elev_ft = wt_elev) %>% 
  #reformat columns into appropriate classes 
  mutate(datetime = as.POSIXct(datetime, format = '%Y-%m-%d %H:%M:%S', 
                                  tz = "GMT"),
    wt_elev_ft = as.numeric(wt_elev_ft),
    wt_elev_m = wt_elev_ft * MetersPerFoot)

Combine S1 data

s1_all <- rbind(s1_se_clean, s1_2019_clean, s1_2020_clean) %>% 
  #remove NA values
  filter(!is.na(wt_elev_m)) %>% 
  #extract year 
  mutate(year = format(as.POSIXct(datetime, format = '%m/%d/%Y %H:%M:%S', 
                             tz = "GMT"),
                  format = '%Y'),
    year = as.numeric(year))

#set min and max year 
min_year = min(s1_all$year)
max_year = max(s1_all$year)

p_s1 <- ggplot(data = s1_all) + 
  geom_line(aes(x = datetime,
                y = wt_elev_m,
                group = 1,
                text = paste0("Date: ", datetime, "\n",
                              "Water Table Elevation (m): ", round(wt_elev_m, digits = 3)))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("S1 Shaft Encoder Water Table Elevation (", min_year, "-", max_year, ")") 
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot 
ggplotly(p_s1,
         tooltip = "text")

S2 Shaft Encoder

  • We are interested in the MaxWTElev column
#set file path to read in data from Box 
filepath = "/Users/miaforsline/Library/CloudStorage/Box-Box/External-MEF_DATA/DataDump/RealTimeData"

#read in the raw data 
s2_se <- readCDL(file = here(filepath, "S2_bogwell_S2BW_Daily.dat"))

s2_se_clean <- s2_se %>% 
  clean_names() %>% 
  #select relevant columns
  select(timestamp,
         max_wt_elev) %>% 
  #rename columns 
  rename(datetime = timestamp,
         max_wt_elev_ft = max_wt_elev) %>% 
  #reformat columns into appropriate classes 
  mutate(datetime = as.POSIXct(datetime, format = '%Y-%m-%d %H:%M:%S', 
                                  tz = "GMT"),
    max_wt_elev_ft = as.numeric(max_wt_elev_ft),
    max_wt_elev_m = max_wt_elev_ft * MetersPerFoot)
  • Plot
#plot 
p_s2 <- ggplot(data = s2_se_clean) + 
  geom_line(aes(x = datetime, 
                y = max_wt_elev_m,
                group = 1,
                text = paste0("Date: ", datetime, "\n",
                              "Water Table Elevation (m): ", max_wt_elev_m))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m)", 
       title = paste0("S2 Bogwell Water Table Elevation (", min_year, "-", max_year, ")")
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
          plot.subtitle = element_text(hjust = 0.5))

#interactive plot
ggplotly(p_s2,
         tooltip = "text")
s2_se_clean %>% 
  #rename columns 
  rename(wte = max_wt_elev_m) %>% 
  #remove rows that have NA wte values 
  filter(!is.na(wte)) %>% 
  summarise(mean_wte_m = round(mean(wte), digits = 3),
            max_wte_m = max(wte),
            min_wte_m = min(wte), 
            sd_wte_m = round(sd(wte), digits = 3)
            ) %>% 
  #t() %>% 
  kable(caption = "S2 Shaft Encoder Bogwell Statistics", 
        col.names = c("Mean WTE (m)", 
                      "Max WTE (m)",
                      "Min WTE (m)",
                      "SD")) %>% 
  kable_material(c("striped", "hover"))
S2 Shaft Encoder Bogwell Statistics
Mean WTE (m) Max WTE (m) Min WTE (m) SD
421.844 422.0916 421.4854 0.115

1:1 Plots

S2 1:1 Plot

#format stripchart data
bog <- bogwell_clean_s2 %>% 
  #remove unnecessary columns 
  select(-peatland, -flag) %>% 
  #create a column to note what type of data this is 
  mutate(type = "stripchart")

#format shaft encoder data 
se <- s2_se_clean %>% 
  #extract date and year 
  mutate(date = format(as.POSIXct(datetime, format = '%m/%d/%Y %H:%M:%S', 
                                  tz = "GMT"), 
                       format = '%Y-%m-%d'),
         date = as.POSIXct(date, tz = "GMT"),
         year = format(as.POSIXct(datetime, format = '%m/%d/%Y %H:%M:%S', 
                                  tz = "GMT"), 
                       format = '%Y'),
         year = as.numeric(year),
         #note data type
         type = "shaft_encoder") %>% 
  #remove unnecessary columns 
  select(-datetime, -max_wt_elev_ft) %>% 
  #rename columns to join the data  
  rename(wte = max_wt_elev_m)

#join the data 
s2_join <- full_join(x = bog, 
                     y = se,
                     by = c("wte", "date", "year", "type"))

#subset the join to the time period where the two data sources overlap 
s2_join_sub <- s2_join %>% 
  pivot_wider(names_from = "type",
              values_from = "wte") %>% 
  #remove rows with NA values 
  filter(!is.na(shaft_encoder), 
         !is.na(stripchart))
p <- ggplot(data = s2_join_sub) + 
  geom_point(aes(x = stripchart, 
                 y = shaft_encoder,
                 alpha = 0.1,
                 group = 1,
                 text = paste0("Stripchart: ", stripchart, "\n",
                              "Shaft Encoder: ", shaft_encoder))) + 
  #plot straight line to compare point spread 
  geom_abline(intercept = 0,
              slope = 1) + 
  theme_classic() + 
  labs(x = "Manual Stripchart", 
       y = "Shaft Encoder",
       title = paste0("S2 1:1 WTE Comparison (", min_year, "-", max_year, ")")
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7),
        legend.position = "none")

#interactive plot 
ggplotly(p = p, 
         tooltip = "text")

Note that this file is being knit as index.html into the climate_bogwell repository in order to update the GitHub pages website, where the plots can be viewed online.